Go down fighting: Short sellers vs. firms
Owen A. Lamont
Graduate School of Business, University of Chicago and NBER
This draft: January 9, 2003
First draft: October 22, 2002
JEL Classification: G14
Key words: mispricing, short selling, short-sale constraints
I thank Nicholas Barberis, Douglas Diamond, Karl Diether, Toby Moskowitz, and seminar
participants at the London School of Economics for helpful comments. I thank Karl Diether and
Yan Zhang for research assistance. I thank the NYSE and Toby Moskowitz for data. I gratefully
acknowledge support from the Alfred P. Sloan Foundation, the Center for Research in Securities
Prices at the University of Chicago Graduate School of Business, the National Science
Foundation, and the Q group.
Corresponding author: Owen Lamont, Graduate School of Business, University of Chicago, 1101
E. 58th St., Chicago IL 60637, phone (773) 702-6414, fax (773) 702-0458.
owen.lamont@gsb.uchicago.edu
The most recent version of this paper is available at:
http://gsbwww.uchicago.edu/fac/owen.lamont
Go down fighting: Short sellers vs. firms
ABSTRACT
I study battles between short sellers and firms. Firms use a variety of ways to impede short
selling, including explicit or implicit legal threats, investigations, lawsuits, and various technical
actions intended to create a short squeeze. Firms that take these actions create short sale
constraints. Consistent with the hypothesis that short sale constraints allow stocks to be
overpriced, firms taking anti-shorting actions have in the subsequent year very low abnormal
returns of about -2 percent per month.
Short sale constraints can allow stocks to be overpriced. Constraints include various
costs and risks, such as the expense and difficulty of shorting, legal and institutional restrictions,
and the risk that the short position will have to be involuntarily closed due to recall of the stock
loan. If these impediments prevent investors from shorting certain stocks, these stocks can be
overpriced and thus have low future returns until the overpricing is corrected. By identifying
stocks with particularly high short sale constraints, one can identify stocks with particularly low
future returns. These constraints are difficult to measure, however, and researchers have
struggled to find appropriate data to test the overpricing hypothesis.
In this paper, I test whether overpricing increases when firms deliberately raise the level
of short sale constraints. Firms (either management or shareholders) can take a variety of actions
to impede short selling of their stock. Firms take legal and regulatory actions to hurt short
sellers, such as accusing them of illegal activities, suing them, hiring private investigators to
probe them, and requesting that the authorities investigate their activities. Firms take technical
actions to make shorting the stock difficult, such as splits or distributions specifically designed to
disrupt short selling. Management can coordinate with shareholders to withdraw shares from the
stock lending market, thus preventing short selling by causing loan recall. These battles between
short sellers and firms can be extraordinarily acrimonious. The following statement from the
sample used in this paper gives a flavor of attitudes toward short sellers: "Your activities are
mean, shameful and loathsome. They are motivated by appalling avarice and greed, and they will
not be permitted to go unanswered."
An example of the various anti-shorting strategies used by firms is provided by Solv-Ex,
a firm that claimed to have technology for economically extracting crude oil from tar-laden sand.
Short sellers claimed that Solv-Ex was a fraud. On 2/5/96, the management of Solv-Ex faxed a
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letter to brokers and shareholders: “To help you control the value of your investment…we
suggest that you request delivery of the Solv-Ex certificates from your broker as soon as
possible.” This suggestion, entirely legal on the part of Solv-Ex, was essentially an attempt at
market manipulation. The letter was an attempt to orchestrate a short squeeze using the stock
lending system (the mechanics of stock lending are described further in section I).
Any shareholder heeding Solv-Ex’s suggestion would have withdrawn his shares from
the stock lending market, potentially forcing short sellers to cover their positions. On 2/2/96,
before the letter, Solv-Ex’s price was at $24.875. By 2/21/96, the price had risen to $35.375,
perhaps due to Solv-Ex’s attempted squeeze. Solv-Ex took other action against short sellers as
well. Later in 1996, Solv-Ex said that it had hired private investigators to find out who was
spreading misinformation about the firm, and subsequently it filed suit against a well-known
short seller, claiming he had spread false information. However, in this case it was Solv-Ex
which was engaged in illegal activities, not the short sellers. Solv-Ex delisted at 7/1/97 at $4.25,
amid an SEC investigation of whether Solv-Ex had defrauded investors. It entered Chapter 11
bankruptcy in 1997, and in 2000 the court ruled that the firm had indeed defrauded investors.
In this case, the evidence is consistent with the idea that Solv-Ex was overpriced in
February 1996, since it subsequently fell sharply. This paper looks at long-term returns for a
large sample of 270 similar firms who threaten, take action against, or accuse short sellers of
illegal activity or false statements. The sample is constructed using publicly observable actions
from news reports and firm press releases.
It turns out that (as in the Solv-Ex case) sample firms have very low returns in the year
subsequent to taking anti-shorting action. Abnormal returns are approximately -2 percent per
month. While this degree of underperformance may seem implausibly large, it is in line with
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other estimates from Jones and Lamont (2002), Lamont and Thaler (2003), and Ofek,
Richardson, and Whitelaw (2002). Thus the evidence is consistent with the idea that short sale
constraints allow very substantial overpricing, and that this overpricing gets corrected only
slowly over many months. As a secondary issue, this paper also examines the short run behavior
of prices around attempted short squeezes. There is weak evidence at best that deliberate short
squeezes can temporarily raise stock prices.
This paper is organized as follows. Section I discusses the general issues of short sale
constraints and reviews related research. Section II describes how the sample was constructed.
Section III describes the sample characteristics. Section IV examines long run returns on sample
stocks. Section V examines short term price movements around attempted short squeezes.
Section VI summarizes and presents conclusions.
I.
Background and literature review
A. Mechanics of shorting stock
To be able to sell a stock short, one must borrow it, and because borrowing shares is not
done in a centralized market, finding shares can sometimes be difficult or impossible. In order to
borrow, an investor needs to find an institution or individual willing to lend. These lenders
receive a daily lending fee from the borrowers, determined by supply and demand in the lending
market.
Brokers have the ability to lend shares of their customers, provided customers have given
written permission. Once a short seller has initiated a position by borrowing stock, the borrowed
stock may be recalled at any time by the lender. If the short seller is unable to find another
lender, he is forced to close his position. This possibility leads to recall risk, one of many risks
that short sellers face.
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There are several reasons that a shareholder might refuse to lend stock, or might
withdraw his shares from the stock lending market. First, if the lender sells his stock, he must
recall his stock loan so that he can deliver his shares to the buyer. Second, in some unusual cases
(which are studied here), firms devise technical actions which force shareholders not to lend.
For example, one firm required shareholders to send their stock certificates to the firm's transfer
agent in order to receive a distribution. An owner cannot send in the certificate unless he is in
physical possession of it. Third, shareholders may refuse to lend their stock because they fear
that by helping short sellers, they will be helping drive stock prices down. This idea (the basis of
the Solv-Ex example) obviously makes no sense in a competitive market where no individual
investor is big enough to affect prices. Fourth, for individual investors, brokers typically only
have the ability to lend out of margin accounts, not cash accounts. Fifth, some institutions do not
have stock lending programs at all, perhaps because they feel their holdings are too small and the
income generated by lending would not be enough to compensate for the fixed cost of setting up
a lending program.
Generally, it is easy and cheap to borrow most large cap stocks, but it can be difficult to
borrow stocks which are small, have low institutional ownership, or which are in high demand
for borrowing. A somewhat paradoxical description of the stock lending market is that it usually
works very well, except when you want to use it, in which case it works terribly. By this I mean
that it can be difficult or expensive to short stocks that many people believe are overpriced and
many people want to short. Of course, this point is the essence of the overpricing hypothesis:
stocks are only overpriced when informed investors are unable or unwilling to short them. No
one would want to short them if they weren’t overpriced, and they wouldn’t be overpriced if they
weren’t hard to short.
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Since the data collection strategy is based on public anti-shorting actions taken by firms
(and in the case of lawsuits depends on firms being able to identify short sellers), it is useful to
consider the conflicting incentives for secrecy faced by short sellers. Short sellers sometimes
attempt to remain anonymous, while other times publicize their activities. On the one hand,
when shorting a stock, one has the incentive to publicize the opinion that the stock is overpriced.
A short seller can communicate his views via press releases, web pages, interviews with the
media, and (if he believes the firm engaged in fraud) tips to the SEC and other law enforcement
agencies. The sooner he can convince other investors that the stock price is too high, the sooner
the price will fall, minimizing holding costs and the price risk.
On the other hand, recall risk, and more generally the cost of maintaining a short position,
give short sellers an incentive for secrecy, since holding costs generally rise when other investors
are also trying to short. For stocks that are hard to short, a short seller would like the stock price
to go down, but he may not want other people to short the stock. The cost and difficulty of
shorting is determined by supply and demand in the securities lending market. If more people
try to short a stock, the cost of shorting might rise and existing stock loans may be called in by
the stock lender. In addition, secrecy might be preferred if the short seller wants to avoid being
sued or harassed by the firm he is shorting.
B. Other short sale constraints
In addition to the problems in the stock lending market, there are a variety of other short
sale constraints. US equity markets are not set up to make shorting easy. Regulations and
procedures administered by the SEC, the Federal Reserve, the various stock exchanges,
underwriters, and individual brokerage firms can mechanically impede short selling. Legal and
institutional constraints inhibit or prevent investors from selling short. For example, Almazan et
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al. (2000) find that only about thirty percent of mutual funds are allowed to sell short, and only
two percent actually do sell short.
In addition to regulations, short sellers also face hostility from society at large. Policy
makers and the general public seem to have an instinctive reaction that short selling is morally
wrong. Short selling is characterized as inhuman, unAmerican, and against God (Proverbs
24:17: "Do not rejoice when your enemy falls, and do not let your heart be glad when he
stumbles."). Hostility is not limited to America. In Malaysia in 1995, the Finance Ministry
proposed mandatory caning as the punishment for short sellers (Law Minister Datuk Syed Hamid
Albar said their caning "will be light, similar to the punishment carried out on juveniles").
Short sellers face periodic waves of harassment from governments and society, usually in
times of crisis or following major price declines as short sellers are blamed. Short sellers are
often thought to be in league with America’s enemies. The general idea is that short selling is
bad, and when bad things happen (such as war) it probably involves short sellers in some way.
For example, the New York Stock Exchange imposed special short selling regulations during
World War I (in November 1917), in response to both a substantial market decline and a fear that
the Kaiser would send enemy agents to drive down stock prices. Jones and Lamont (2001) and
Jones (2002) discuss another historical episode following the crash of 1929. This historical
pattern has continued in recent years, following both the terrorist attack of September 11, 2001
and the more general market fall starting in 2000. Press reports indicate that authorities in
Britain and Japan have sought to discourage shorting.
C. Overpricing
Short sale constraints can prevent negative information or opinions from being expressed
in stock prices, as in Miller (1977). Although constraints are necessary in order for mispricing to
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occur, they are not sufficient. Constraints can explain why a rational arbitrageur fails to short the
overpriced security, but not why anyone buys the overpriced security. To explain that, one needs
investors who are willing to buy overpriced stocks. Thus two things, trading costs and some
investors with downward sloping demand curves, are necessary for substantial mispricing.
This willingness to hold overpriced stocks can be interpreted either as reflecting irrational
optimism by some investors, or rational speculative behavior reflecting differences of opinion.
Harrison and Kreps (1978) construct a model with rational investors where differences of
opinion, together with short sale constraints, create a “speculative premium” in which stock
prices are higher than even the most optimistic investor’s assessment of their value (see also
Duffie et al, 2002). These differences of opinion can be interpreted as arising from different
prior beliefs which rationally converge as information arrives (Morris 1996), or as irrational
overconfidence (Scheinkman and Xiong 2002). In any case, short sale constraints generate a
pattern of overpriced stock leading to subsequent low returns.
A variety of empirical evidence confirms the prediction that binding short sale constraints
lead to low returns, although much of the evidence is circumstantial because it is hard to observe
the level of short sale constraints for different stocks. Looking across stocks, the prediction is
that when constraints bind more tightly, subsequent returns will be lower. One can test this
hypothesis either by finding stocks with higher constraints (if constraints vary across stocks), or
finding stocks with higher unexpressed shorting demand (if the demand for shorting varies
across stocks). The basic idea of looking at shorting demand is that some investors want to short
a stock but are impeded by constraints, thus the stock is overpriced. If one can estimate the size
of this group of investors, one can measure the extent of overpricing. In practice, measures of
shorting constraints and shorting demand tend to be highly correlated since both are reflecting
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the same mechanism that constraints prevent informed investors from immediately correcting
overpricing.
One measure of short sale constraints is the cost of shorting reflected in the stock loan
market. A variety of recent papers study the market for borrowing stock (D’Avolio (2002),
Geczy et al. (2002), Mitchell et al. (2002), Ofek and Richardson (2002), and Reed (2001)),
unfortunately using sample periods of short duration. Jones and Lamont (2002), using a longer
sample of sample shorting costs, show that stocks that are expensive to short or that enter the
lending market have high valuations and low subsequent returns. Indirect cost of shorting can
come from options as in Figlewski and Webb (1993), Sorescu (2000), Lamont and Thaler
(2003), Ofek, Richardson, and Whitelaw (2002).
Proxies for shorting demand include breadth of ownership (Chen, Hong, and Stein
(2002)), dispersion of beliefs (Diether, Malloy, and Scherbina, 2001), or the level of short
interest (Figlewski (1981), Figlewski and Webb (1993), and Dechow et al. (2001)).
Unfortunately, using short interest as a proxy for shorting demand is problematic, because the
quantity of shorting represents the intersection of supply and demand. The quantity of shorting
should respond to both the cost and benefit of shorting the stock, so that stocks that are very
costly to short will have low short interest. Stocks that are impossible to short have an infinite
shorting cost, yet the level of short interest is zero. Lamont and Thaler (2003), for example,
examine a sample of technology carve-outs that appear to be overpriced. In their sample, the
apparent overpricing and the implied cost of shorting fall over time, while the level of short
interest rises. Thus short interest can be negatively correlated with shorting demand,
overpricing, and shorting costs. The problematic nature of short interest leads to weak empirical
results.
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D. Reaction to news
Another strand of literature looks at the reaction of prices to news about short sales. In
rational models such as Diamond and Verrecchia (1987), informed investors sell short, but once
short interest is announced stock prices should immediately adjust to take into account the
negative information. Consistent with this idea, Aitken et al. (1998) find that stock prices fall
immediately in response to announced increases in short interest.
The anti-shorting actions studied here might reveal something both about the presence of
informed pessimists, and the information possessed by the firm. For example, perhaps only
firms that believe themselves to be overpriced firms engage in anti-shorting actions, since
underpriced firms know that the market will eventually recognize their true worth. As in the
Solv-Ex case, empirically many firms accuse short sellers of fraud, but are in fact themselves
guilty of fraud (“The lady doth protest too much, methinks.” Hamlet. Act iii. Sc. 2.). One short
seller in the sample noted “we can look at a company that is attacking us. When we have the
goods on that company, it tells us we are onto something. The louder they scream, the better the
short.”
While the information revealed by anti-shorting actions is an interesting topic, it isn’t
explored here. Rather, the hypothesis tested is whether anti-shorting actions lead to predictable
low returns. Under the hypothesis of efficient markets with no frictions, price should
immediately adjust to new information, and post-event returns should be unpredictable. The
empirical question studied here is not reaction to news, but rather long-term returns based on
lagged public information.
II.
Constructing the sample
I searched Lexis, Dow Jones Interactive, and other text sources for episodes featuring
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reported disputes between short sellers and firms, ending in June 2002. These sources contained
newspaper and magazine articles, newswire items, transcripts of broadcast media, and press
releases. “Firms” were in most cases firm management, but in a few cases were large
shareholders of the firm. I searched on key words such as “short seller”, “lawsuit,”
“conspiracy,” and so on. Finding the relevant events was a time-consuming and labor-intensive
process that involved reading several thousand pages of text. Coding the events sometimes
required judgment calls, and information was often incomplete or ambiguous.
I find 326 events from 270 different firms, the earliest event in March 1977, and the latest
in May 2002. Media coverage is thinner in earlier years, and 78% of the events occur after 1990.
In addition to events, I also have (up to June 2002) returns, market equity, and volume from
CRSP, and book values from Compustat. To get in the sample, firms have to be in CRSP
sometime within the first 12 months after the event (this excludes OTCBB stocks, which are the
source of many extraordinary anti-shorting episodes). I exclude cases involving merger arbitrage
or convertible securities arbitrage.
The sample includes events where the firm mounts some sort of defense against short
sellers, or accuses short sellers of wrongdoing (and thus implicitly raise the threat of legal
action). The sample includes nine types of events, sorted into three categories. The first
category is belligerent statements about short sellers, ranging from threats of legal action to
detailed refutations of short seller’s claims. The second category is actually taking legal or
regulatory action against short sellers. The third category is actually taking technical action to
prevent short selling. Table I lists the different events types of events.
A. Belligerent statements
The first category, belligerent statements, is when the firm claims that short sellers are
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acting improperly to cause the stock price to go down. I include in this category cases when the
firm expends some effort refuting or denouncing short sellers, but falls short of taking specific
actions. Media often describe these statements as “declaring war on the shorts,” although only
verbal shots are fired in this war. Belligerent statements often contain explicit or implicit threats
of legal action against short sellers. Although belligerent statements themselves do not impede
short sellers, they do indicate a greater likelihood that the firm will take some anti-shorting
action in the future.
Belligerent statements include any case in which firms explicitly accuse the short sellers
of making illegal or improper actions or factually incorrect statements. I exclude from the
database cases where the firm mentioned short sellers but did not accuse them of improper
action. These non-belligerent statements included “Rouse has written letters to investors known
to be shorting its stock, inviting them to come down to Columbia and talk” or "sure it bothers me
that anybody would bet against us, but I've gotten used to it...we've come so far so fast that some
people just don't believe we're for real yet."
Belligerent statements come in three types. The first is when the firm claims some sort of
conspiracy by short sellers, and includes claims of illegal shorting or stock price manipulation.
A typical statement is “We continue to firmly believe that ...stakeholders have been victimized
by stock manipulators.” This category also included claims that short sellers were harassing or
manipulating customers, investors, or suppliers.
The second type (which was often difficult to distinguish from the first) was the claim the
short sellers were spreading misinformation about the firm. Typical cases claimed lies, rumors,
or planting negative stories in the media. To get into the database, firms had to either explicitly
discuss the role of short sellers in spreading these lies, or to be rebutting someone who was
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publicly acknowledged to be shorting the stock. A typical statement is “These false rumors
regarding both the company and its products have been spread by short sellers of the company's
stock.” Often these responded to media reports, particularly those appearing in Barron’s or by
CNBC’s Dan Dorfman. Events sometimes involved lengthy point-by-point rebuttals of media
reports.
Third, firms state they are considering their legal options and thus threaten potential legal
action against short sellers or critics. A typical statement is “The Board of Directors has directed
legal counsel to protect the Company's integrity and reputation and they will be assessing the
legal remedies which the Company may pursue.” This category also includes cases where the
firm states they are undertaking their own investigation of short sellers, including hiring outside
counsel or hiring private investigators. A typical statement is “The company, however, believes
its stock has been artificially depressed from ‘illegal short-selling’ and is conducting an
investigation.”
B. Legal actions
I break legal or regulatory actions into two types of events. The first is when firms publicly
announce that they are requesting an investigation by regulatory authorities, usually the relevant
exchange or the SEC: “We have observed considerable short selling in the stock and have
requested the New York Stock Exchange and the Securities and Exchange Commission to
investigate this activity.” This category also includes cases where the firm claims the SEC is
already investigating short sellers. It is sometimes the case that when short sellers claim the SEC
is investigating the firm for accounting shenanigans, the firm will respond by claiming that the
SEC is investigating the short sellers for price manipulation.
This category also contained five cases when the media (as opposed to the firm) reports
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that the SEC or exchange actually are investigating short sellers, for example: “The Securities
and Exchange Commission is investigating a Pennsylvania company's allegations that it is being
targeted by false press releases and phony Internet postings possibly intended to damage its stock
price.” Although not every case reveals (as in this example) that the firm alleged wrongdoing, in
each of the cases the reports occur after other anti-shorting activities by the firm. It seems
reasonable to infer that the firm complained to regulators. This category also includes four cases
involving 1989 Congressional hearings about short selling. Media reports indicated that the
hearings were initiated by disgruntled firms (these hearings are discussed further in subsection
F).
The second type of legal action is lawsuits initiated by the firm or by shareholders against
short sellers or critics. Sometimes these lawsuits were against short sellers or brokers, and
sometimes against media outlets or analysts. For example “A shareholder suit was filed today
alleging that a number of short sellers and hedge funds conspired with Alan Abelson, columnist
for Barron's, a weekly business magazine, to trade shares of Technicare.” In one case I only
observe the outcome of litigation in the case when the out of court settlement involves public
humiliation: “The Wall Street Journal contained a grovelling apology to Livent from Alex
Winch, a noted Toronto-based investor who specializes in shorting stocks that he expects to see
fall from grace... As part of a legal settlement, Mr. Winch ran ads that explained he erred in
allegations about Livent's accounting policies contained in a letter to Forbes magazine last
September.” Of the 35 lawsuits, 7 were “cybersmear” lawsuits involving electronic postings by
unknown persons. These lawsuits usually speculated that the posters were short sellers: “In its
libel suit, Hollis-Eden alleges the defendants, named only by their screen aliases, could be
disgruntled former employees and shareholders or people working with short sellers to
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manipulate the stock.”
C. Technical actions
Firms can make it difficult to short their stock by disrupting the ability to borrow shares.
One technical action taken by firms in the sample was to switch stock exchanges from the
NASDAQ to AMEX or NYSE. For much of the sample period, NASDAQ was perceived as
being more lax in its short selling rules (it did not adopt the uptick rule until 1994). The sample
includes six firms announced that they were changing or applying to change exchanges explicitly
to impede short selling (event dates range from 1988 to 1996). Of course, firms switch
exchanges for many reasons, and certainly for most firms the primary motivation is not to deter
short selling. So this category only includes exchange switches where the firm specifically states
that impeding short selling is one of its motivations.
Second, as in the example of Solv-Ex (discussed in the introduction), 29 firms urge or
suggest that shareholders collectively withdraw their shares from the stock lending market. A
prominent example is Irwin Jacobs, an investor who owned 5% of Conseco. In July 2000 he
waged a personal campaign against short sellers, spending $400,000 for full-page ads in the New
York Times and Wall Street Journal, calling on fellow shareholders to take their shares "inhouse." (02/19/2001 Forbes).1
Third, firms attempt in a variety of ways to get shares into the hands of friendly owners,
who presumably will not lend their shares. I classify 7 events of this type, and it includes a
number of loosely related actions. Again, all require that the stated action be explicitly in
response to short sellers or taking place within the context of a battle with short sellers. Two are
when the CEO of the firm announces that he is setting up an entity to buy and sell firm shares,
using his own personal money. Three are when the firm announces it is repurchasing its own
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shares (I require that the announcement specifically mention short sellers). One is when it is
announced that a large shareholder is withdrawing his shares from the lending market. One is
when a firm announces that the employee stock ownership plan is acquiring shares. A last is
when a firm offers to lend money to its shareholders to replace margin loans, so that the
shareholders can place their stock in cash accounts (brokers typically can lend shares of their
customers in margin accounts but not cash accounts).
The last category contains miscellaneous trading-related actions to impede short selling.
There are six cases where firms do splits or distributions which are apparently designed to force
short sellers to close out their positions. For example, one firm required holders to send their
stock certificates to the firm's transfer agent to be eligible to receive a stock dividend. This
action would force recall of stock loans. Another firm did what was effectively a 1.1 for 1 stock
split, with the same intention.
In three other cases, firms initiated trading halts explicitly in response to short sellers.
The policy of the New York Stock Exchange is that firms should warn the NYSE ten minutes
prior to major announcements, and sometimes the NYSE halts trading for a brief period, often 10
or 20 minutes, while the information is disseminated. One of these cases is described by Asensio
(2001). After Asensio had released a negative report on a firm, the firm informed the NYSE that
a major announcement was forthcoming and trading stopped at 10.44am. Trading did not finally
resume until 3.52pm, eight minutes before the exchange closed, and in those eight minutes the
price rose. According to Asensio, “someone had done a helluva job rallying the troops...By that
time the institutions had galvanized enough buyers to run up the stock.” The firm did not make a
major announcement, but only released a press release denouncing Asensio.
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D. Limitations of the database
The database is surely an incomplete record of anti-shorting action taken by firms. First,
it does not reflect private anti-shorting actions, such as private lawsuit threats made by firms.
Many events only appear in the sample because they are deemed interesting or newsworthy. It is
undoubtedly the case that many anti-short selling actions do not get into the database because
they are not reported in the media. In addition, the search process contained a somewhat random
element, as researching one event sometimes led to another event. As a result, the database is
only a partial reflection of reality, and in particular it is likely that when firms take multiple
actions, only some of them are reflected in the database. Coverage is particularly incomplete in
the early years of the sample.
Some firms appear multiple times in the database since they took multiple actions. For
this reason, the totals in the rightmost column of Table I are not always the sum of the individual
categories. My coding strategy is to stop collecting belligerent statements on the first date that
technical or legal events take place, so by construction the sample contains no belligerent
statements after a legal or technical event. Probably many of the firms making belligerent
statement subsequently take technical/legal actions that are not reflected in the database because
they are not reported in the media.
E. Excluded cases
I exclude a variety of other forms of harassment of short sellers and suppression of
criticism, since these anti-shorting actions were difficult to systematically classify. These cases
range from the farcical to the tragic.
According to sworn testimony, Charles Keating Jr. (of the famous Keating Five) wanted
to buy every copy of Forbes magazine sold near branches of his Lincoln Savings, because the
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magazine contained critical comments. In other cases, firms attempt to prevent short sellers from
asking questions at conference calls or annual meetings. In one case, when a short seller tried to
ask a question at the annual meeting of Cineplex Odeon, he was drowned out by a “prolonged
and very loud coughing fit” which made his question inaudible (both Keating’s firm and
Cineplex are included in the sample because of other reported incidents).
There are various reports of short sellers (and sometimes critical journalists or analysts)
receiving death threats, requiring bodyguards, and arming themselves. In at least one case,
someone may have been killed because of short selling. The case involves the Tel-Com Wireless
Cable TV, whose official spokesperson was Ivana Trump. On 12/14/1998, Barron’s reported
that “several terrified investors told Barron's and the police that their families had been
threatened by convicted criminals who accused the investors of selling short” (the firm gets in
the sample, not because of this accusation, but because of the firm’s subsequent rebuttal). A year
later, 11/01/1999 , Barron’s reported that one of the threatened individuals had been found
murdered, execution-style, in Colts Neck, New Jersey.
The sample includes only explicit anti-shorting actions. I exclude from the sample some
events which seemed to be anti-shorting but where the participants denied trying to hurt short
sellers. For example, octogenarian potato tycoon J.R. Simplot was a major shareholder of
Micron Tech, and also controlled an unrelated firm, the J.R. Simplot Company. He offered to
his workers at Simplot the following employee benefit: "Mr. Simplot will make whole any loss"
on Micron shares they buy (essentially giving employees a put option as long as they bought
shares). Although it was speculated in the press that this was intended to hurt short sellers (by
increasing share prices and putting more shares into friendly hands), the J.R. Simplot Company
denied this allegation.
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In addition to the public manipulation of the stock loan market discussed previously,
there are also allegations of private manipulation by institutional owners. One such accusation
(09/24/1996 Wall Street Journal) is that Fidelity Investments deliberately withdrew its shares of
Chesapeake Energy from the stock loan market in order to drive up the price. Fidelity denied
this allegation.
An extreme example of stock loan manipulation the “corner”. Suppose B borrows shares
from A and sells them short. Now A acquires 100 percent of the shares (or at least 100 percent
available in the market) of the stock and demands that B return the shares. In this case the only
way B can return the shares is to buy them (at an inflated price) from A. Corners probably
occurred (in US equity markets) more frequently in the late 19th and early 20th century, a
prominent late case being the Stutz Motor corner of 1920 (Meeker, 1932). Two recent cases in
the US occurred in the late 1980’s. The president of Southland Communications was tried and
convicted for driving his stock price up in 1989 by cornering the firm's stock through secret
trades. In another case, the SEC determined that two individuals had orchestrated a corner in
Chase Medical in 1988, and that they controlled 109% of the public float. Neither of these cases
are reflected in the sample since they were not public anti-shorting actions.
F. Dubious ventures
Short sellers often claim that the firms they target are either frauds or failures. A striking
feature of the sample is that ex post, many of the firms taking action are indeed fraudulent or
have a product that simply does not work (often both). In addition to incompetent products,
many of the frauds seem incompetent themselves. In one case, short sellers noticed that the
firm’s quarterly statements did not add up to the annual. In another case, a firm press release
identified an executive as “Larry West,” when in fact his name was Lawrence West Melquiond,
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who had reportedly been associated with many previous business failures. Melquiond responded
that his real name was omitted because it was “too difficult to spell and pronounce." Since
Melquiond’s correct name was listed in SEC filings, this attempt to hide information seems silly.
Consider, for example, one episode from the sample. In 1989 Congress held hearings
about the evils of short selling, featuring testimony from supposedly victimized firms. During
the hearings, Congressman Dennis Hastert (later speaker of the US House of Representatives),
described short selling as “blatant thuggery”. But who was the victim and who was the thug?
Four firms in the sample participated. Of these, one was bankrupt by 1990. By 1991, a second
was bankrupt and its top executives charged with fraud by the SEC. A third had its President
charged with fraud by the SEC in 1998 (the individual had been President of the firm since
before the 1989 hearings). Thus two out of four of the firms were bankrupt within two years,
and two were shown to be fraudulent. This example suggests that short sellers play an important
role in detecting fraud.
III.
Characteristics of the sample
Table II shows the characteristics of the 270 firms in the sample. To avoid double
counting, I show the characteristics of the firms in the month prior to the month in which the first
event takes place.2 For example, in the “all events” row, the statistics are for the month end
preceding the first event, even if multiple events take place. The table shows the percentile
ranking of the variable relative to all stocks in CRSP in the same month.
Looking first at size, the average ranking for all events is 67 percent, so that event firms
are above median relative to the universe of firms in CRSP. This characteristic may reflect the
fact that larger firms are more newsworthy. Market-book ratios show that event firms are growth
firms, since they are in the upper quartile of valuation. Strikingly, event firms have very high
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trading volume in the month before the event (trading volume is measure as share turnover
relative to other stocks on the same exchange). Part of this high volume of 87 percentile may
reflect information released around the event date. However, 12 months before the event month,
it is still the case that event firms have percentile turnover of 77 percent. One interpretation is
that volume proxies for differences in opinion (agents trade when they disagree), and thus these
are firms where investors have very dispersed opinions. Under short sale constraints, high
volume may predict low future returns (see for example Diether 2002).
Looking at pre-event returns, the differences are less dramatic. Prior one year returns are
slightly above median and prior month returns are about the same as the median stock. Thus
events do not seem to be triggered by extreme stock price movements at the monthly or yearly
level. In summary, event firms are high volume, growth firms of above median size.
IV.
Long run returns
A. Event time returns
Under the overpricing hypothesis, post-event returns should be low. Table III shows monthly
returns subsequent to the event, for different time horizons. It shows event time returns with a
very simple risk-adjustment procedure: returns are market-adjusted by subtracting out the return
on the CRSP value weighted aggregate (later, section B shows calendar time returns with
different risk adjustments). The table shows average monthly returns earned by buying the event
stock on the last day of the month in which the event is publicly observed. Thus it measures
post-event long-term performance.
First looking at the all event, one year cell, Table III shows that event firms have very
low returns.3 In the twelve months subsequent to any event, firms have returns that are a
whopping 2.34 percent per month lower than the market, and this difference is highly statistically
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significant. In terms of the magnitude, the two percent per month is in the same ballpark as the
large estimates found in other cases of extreme short sale constraints. Jones and Lamont (2002)
found that highly constrained firms had returns that were lower than other firms by about one
percent per month in the year subsequent to becoming constrained, for the period 1926-1933.
Lamont and Thaler (2003) find abnormal returns of -10 percent per month over an approximately
six month period for a sample of six cases involving short sale constraints, for the period 19982001. Ofek, Richardson, and Whitelaw (2002) find abnormal returns of about -2 percent per
month for a sample of short sale constrained stocks, for the period 1999-2002.
Looking now at the different time horizons, two facts emerge. First, the low returns are
highly persistent, and continue for years. In the 36 months after any event, the market adjusted
returns are -1.51 percent per month, so that the cumulative fall is 42 percent. The last column
shows that, even after excluding the first year (so that the sample is firms which had an event in
the past three years but not the in the last year), market adjusted returns are a substantial -0.95
percent per month. While this number has a t-statistic that is less than two, partially because the
sample contains fewer observations since many have gone bankrupt or been delisted, it is
economically large and one can reject the appropriate one-sided null hypothesis that returns are
non-negative at a level of p=5.5%. Thus the time pattern is consistent with overpricing that is
only slowly corrected through time. The effect is not primarily a short-term under-reaction to
bad news that gets quickly corrected. Rather, it is a long-term overpricing phenomenon, with
time pattern similar to the value effect.
Second, it is clear that the returns are lowest immediately after the event, and gradually
the effect weakens over time. Thus there is no evidence in Table III to support the idea that one
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should buy stocks after an event occurs, in anticipation of a short squeeze (Section V examines
in more detail a subset of these events, and look at daily returns around event dates).
Looking across the different types of events, there does not seem to be much difference in
mean returns. In drawing inferences about the effect of short sale constraints, the legal and
technical categories may be more reliable, since the belligerent statement category is somewhat
more ambiguous. Belligerent statements are just cheap talk, and require more researcher
judgment to define the event. In contrast, technical and legal events are tangible actions. Since
all three categories of events predict large and statistically significant underperformance of
returns, it is not necessary to use the belligerent category to draw inferences about the effect of
events. Even excluding belligerent statements, there is strong evidence that anti-shorting events
are followed by very low returns.
Although legal and technical events have slightly lower post-event returns, this difference
is small and not reliable. In regressions not shown, there is no statistically significant difference
in returns between the three classes of events (belligerent, legal, and technical). Measurement
error may be responsible for some of these similarities, since some belligerent firms probably
also take unobserved legal or technical actions. Since there are not detectable differences
between the categories, the rest of the section discusses the “all events” row only.
The effect is robust to different methods. First, the calculations in Table III include the
effects of delisting returns: 30 of the 270 firms delist in the twelve months after the event.
Excluding these delisting returns, the average market adjusted return at the one year horizon is
-2.05 instead of -2.34.4 Second, the effect is large and significant both before and after 1990.
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B. Calendar time
Table IV reports equal weighted calendar time portfolio returns for the one year horizon,
showing both market adjusted returns and two other adjustment methods. One should be
cautious using calendar time returns here, since in the early years there are very few firms per
month in the event portfolios, often only one. As it turns out, calendar time and event time
calculations give about the same answer. The first column shows market adjusted returns. For
all events, market adjusted returns are -2.88 percent per month over the 257 months of the
sample, slightly higher than Table III’s number.
As shown in Table II, event firms tend to be growth firms, so it is important to test
whether the return effect reflects only this fact. The next column of Table IV shows
characteristic adjusted returns which control for size, value, and momentum. Following Daniel,
Grinblatt, Titman, and Wermers (1997), it subtracts from the event firm return the return on a
portfolio of firms matched on market equity, market-book, and prior one-year return quintiles (a
total of 125 matching portfolios).5 As expected, this adjustment decreases the magnitude of the
effect, but the remaining effect is still strikingly large and significant.
An alternate way to adjusted for value, size, and momentum is to calculate alpha’s from a
factor regression. The last column of Table IV reports intercepts from a four factor model where
the factors are the market, size, and value as in Fama and French (1993), augmented with a prior
year return factor as in Carhart (1997).6 Again, after this risk adjustment, a large and significant
effect remains.
The last row of Table IV shows the value weight instead of equal weight returns. Here
the returns are somewhat less dramatic, though the effect is still substantial and statistically
significant (it is significant for the two-sided hypothesis for the factor regression, and at the one-
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sided hypothesis for the market adjusted and characteristic adjusted means). The fact that value
weighting gives a smaller effect is consistent with the idea that smaller stocks are more difficult
to short and thus can be more overpriced.
Table V shows the coefficients from the four factor regressions. Most notable are the
coefficients on value and momentum factors. For the value factor, the coefficient is
insignificantly different from zero, suggesting that these firms’ returns do not behave much like
other growth firm returns. For the momentum factor, the coefficient is significant and negative,
suggesting these firms returns are behaving like firms with negative momentum. This finding is
not surprising: these firms have falling prices during the 12 month post-event period in which
these factor loadings are estimated, so one would expect their returns to be correlated with other
firms with falling prices. Table II showed that prior to the event, the firms had positive (not
negative) momentum since their prior year returns were above median. If one re-estimates the
regression for the one month after the event (rather than the 12 months), one gets a positive
loading on the momentum factor. So going into the event the firms have positive momentum,
after the event they have negative momentum.
In summary, event firms have extremely low subsequent returns that are not explained by
value, size, or momentum, using either characteristic adjustment or factor models. Event firms
have returns are two to three percent lower than non-event firms.
V.
Short squeezes
This section examines in more detail the events where firms attempt to coordinate a short
squeeze by asking their shareholders to withdraw shares from the stock loan market. I focus on
this event since it is the most common of the technical events designed to create a squeeze.
Journalists and market participants often cite short squeezes as explanations for
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movements in stock, bond, and commodity markets. One common definition is that a short
squeeze occurs when the price of a security rises, causing the short sellers to experience a decline
in net wealth. Either because of lower wealth, increased risk aversion, fear of further price rises,
or margin calls, the short seller then covers his short position, increasing the demand for the
security and driving the stock further up. In contrast, the definition of a short squeeze used here
is when a short seller is involuntarily forced to cover his short position because he is no longer
able to borrow the security. Most of the literature on short squeezes focuses on either theoretical
issues or empirical findings in the bond, commodity, and derivative markets (for example Jarrow
1992, 1994, Jegadeesh 1993, Kumar and Seppi 1992, Merrick, Naik, and Yadav 2002, Nyborg
and Sundaresan 1996, and Pirrong 1993, 2001). D’Avolio (2002), using data from the stock
lending market, finds little evidence for short squeezes in his 18 month sample.
Wall Street wisdom suggests that high short interest stocks are good to own because they
may rise due to a short squeeze. According to this view, one should be willing to buy overpriced
stock in anticipation of a short squeeze that will drive prices still higher. An example of this idea
is given in the case is GenesisIntermedia (which also illustrates direct manipulation in an attempt
to enrich specific individuals). On April 25, 2001, Chief Executive Ramy El-Batrawi sent a
letter to shareholders asking them to contact their brokers and have shares put into cash accounts,
where brokers would be unable to lend them to short sellers. Between April 25 and May 25, the
stock rose 39% from 12.04 to 16.7.
GenesisIntermedia was 92% owned by a group of four insiders, including 45% owned by
its chairman, Saudi financier and arms merchant Adnan Khashoggi (a central figure in the IranContra affair). According to the LA Times (5/11/2001):
"I think that after our chairman sent out his letter, our shareholders started to take
possession of some of their shares, forcing the shorts to cover their positions," said
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Douglas Jacobson, the company's chief financial officer. "There also may be upward
pressure on the stock from people seeing the letter and then buying in anticipation that
the short squeeze will get more dramatic." Indeed, Jacobson conceded that Khashoggi
might be among those investors and might eventually sell shares to bring his holding
closer to the third of the company he owned previously. According to documents filed
with the Securities and Exchange Commission, Khashoggi purchased more than 60,000
shares at about $11.30 each since El-Batrawi's letter. "It could end up being very good
for him to have the shorts buy back shares from him," Jacobson said.
There are 29 cases where firms attempt a coordinated loan withdrawal. For the purposes
of this section, I discarded five of these. Two had event dates that were identified only by month
(these events are reflected in the long term returns shown in section IV). Three have no clear
event date, since media reports simply mentioned that discouraging lending was a continuing
policy of the firm (these events are reflected in the long term returns shown in section IV, with
the event date set equal to observation date). Of the remaining 24 cases, 22 were clearly public
information that was available in real time, and in two cases the event was semi-private, and only
appeared in the media subsequent to the event (these two cases are reflected in the long term
returns shown in section IV, with the event date set equal to observation date).
Before reviewing the evidence on these actions, it is instructive to consider how these
events differ from other events typically studied in financial economics. First, these events are
not clean and exogenous. Unlike, for example, earnings announcements, which come at preannounced and regular intervals, these events are entirely endogenous and occur partly in
response to the level of stock prices. Other events (including other anti-shorting activities) are
also happening in the same window. Second, these events are not only information events, but
also technical events reflecting the operations of the stock loan market. In a world with frictions
(as opposed to the standard assumption of frictionless and informationally efficient markets), it is
not clear whether one would expect prices to immediately reflect the short squeeze, or to slowly
respond as the squeeze is put in effect. It presumably takes some time for investors, after
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receiving the suggestion, to contact their brokers to withdraw their shares from the loan market,
and short sellers who have their loans withdrawn have several days to deliver the shares. Last,
the evidence will necessarily have a limited ability to measure the efficacy of anti-shorting
actions. It is hard to tell whether an anti-shorting action has any effect because one doesn’t know
how much shorting there would be in the absence of action. It could be that prices would have
fallen faster if the action had not been taken. All that can be said is that one can be fairly
confident that the anti-shorting actions did not make life easier for short sellers.
Figure 1 shows volume around event days (days are trading days, not calendar days).
The volume is expressed as the percentile of exchange-adjusted turnover relative to the CRSP
universe. As shown in Table II, event firms tend to be high volume firms in general, and before
the event the firms tend to be in the top quartile of turnover. Volume spikes up on the event date,
suggesting that something is indeed happening on that day. Even with only 24 firms, this spike
in volume on the event day is statistically significant. D’Avolio (2002) reports that high recall
days have extremely high trading volume, consistent with the event causing recall of stock loans.
Of course, this spike does not prove that the anti-shorting action is causing volume to increase; it
could be that the action is taken in response to higher volume or to some other unobserved event.
Figure 2 shows cumulative average market adjusted returns around event days. Since
returns are so volatile and there are only 24 firms, standard errors are large, and one would need
incredibly large mean returns in order to get statistical significance. Figure 2 shows that
cumulative returns are not incredibly large, and thus there is generally nothing in Figure 2 that is
statistically significant. Looking at the point estimates, there is some evidence that the actions
do succeed in slightly raising prices temporarily. Cumulative returns are 4.6 percent from day t5 to day t+5. By day t+20, a month after the event, however, nothing much has changed. The
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most notable feature of Figure 2 is the pre-event increase in returns, reflecting perhaps insider
buying in anticipation of the squeeze, or perhaps other events. Figure 2 also shows cumulated
alpha’s from the Fama-French 3-factor model estimated on daily data, which are about the same
as cumulated average market adjusted returns.
Additional evidence comes from examining the level of short interest, a statistic that is
available only monthly. For the short interest observed immediately prior to the event, the
average short interest as a fraction of outstanding shares is 11.3 percent, a level far higher than
most stocks (Dechow et al (2001) report that less than two percent of all stocks have short
interest of greater than five percent). One month later, after the event, the level is 11.7 percent.
Thus the event does not succeed in lowering short interest (the level continues to climb to 12.8
percent in the next month).
Summarizing, these actions do not seem to have much ability to raise prices or decrease
short interest, although it could be that absent these events prices would have fallen more and
short interest would have increased more. What is clear is that this ability, if it exists, is only
temporary, and that long-term returns on these stocks are abysmal.
VI.
Conclusion
The evidence shows that when firms take anti-shorting actions, their stock returns are
extraordinarily low over the subsequent months and years. The evidence confirms the hypothesis
that short sale constraints allow stocks to become overpriced. While the underperformance of -2
per month is very large, it is similar in magnitude to the -1 to -10 range found in other studies of
stocks with very high short sale constraints, such as Jones and Lamont (2002), Lamont and
Thaler (2003), and Ofek, Richardson, and Whitelaw (2002).
The 25 years of evidence studied here is valuable because of the difficulty of finding
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direct data on short sale constraints. Jones and Lamont (2002) find data for six years (19261933) while Lamont and Thaler (2003), and Ofek, Richardson, and Whitelaw (2002) studied data
for a few years around the year 2000. Each one of these four data sets has unique characteristics,
and it is conceivable that any one result reflects chance or an unusual sample period. But taken
together, the evidence shows that in extreme cases where short sellers want to short a stock but
find it difficult to do so, overpricing can be very large.
An alternate interpretation of the results are that anti-shorting actions are a signal that
insiders know that the firm is overvalued, so that the low returns reflect inside information
instead of short sale constraints. While it is certainly true that anti-shorting actions may reveal
negative inside information, this story does not explain why it takes so long for the information
to be reflected in stock prices. With no frictions, the information should be immediately
incorporated. In contrast, short sale constraints provide an explanation for the slow reaction of
prices to information. Since the effect persists for years, the low returns are not primarily a
short-term under-reaction to bad news. Rather, the low returns reflect persistent overpricing.
Most firms do not take anti-shorting actions, and for most large cap stocks it is not
difficult to sell short. Thus one cannot conclude from the evidence that short sale constraints are
pervasive phenomena in stock pricing. What we do know is that for most stocks, very little short
selling occurs (relative to other trading activity) and most investors never go short. Thus
something is constraining short selling, perhaps lack of knowledge about shorting, institutional
constraints, risk, or cultural issues. Generalizing from the narrow (but dramatic) evidence
presented in this paper, one can speculate that these more general short sale constraints also
affect stock prices.
What should an investor do if he sees a firm taking an anti-shorting action? The evidence
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here cannot say whether it is a good idea to short this stock. Although one can make large gross
returns on average if one is able to maintain a short position for months, maintaining the short
position might be difficult or expensive. Even if there are no problems borrowing the stock, a
short seller may need to spend time and money dealing with lawsuits and investigations. It is
unclear how these costs and benefits net out. It is clear, however, that it is a bad idea in general
to own stock in a firm that is taking these actions. Investors seeking high returns should look
elsewhere.
The fact that firms take anti-shorting actions tells us that they must believe these actions
are worth doing. A substantial body of research studies whether firms opportunistically take
advantage of mispricing by issuing equity when it is overpriced and buying it back when it is
underpriced (for example Loughran and Ritter, 1995). Corporate managers certainly say they are
trying to time the market (Graham and Harvey (2001)). However, it is difficult to discriminate
between the hypothesis that firms are responding to rational changes in discount rates as opposed
to over- or under-pricing (see for example Lamont (2000), Polk and Sapienza (2001)). The antishorting actions studied here show that firms are not just passively responding to stock prices,
but are in fact actively trying to prop up their stock prices. In this respect the phenomenon is
similar to the evidence on earnings accruals, which can be interpreted as firms actively
manipulating accounting numbers to cause overpricing (see for example Chan et al, 2001).
Similarly, firms encourage analysts to issue optimistic forecasts, and reward optimistic analysts
with investment banking business (see for example Michaely and Womack 1999).
A notable feature of the data is that many of the sample firms are subsequently revealed
to be fraudulent. This paper has presented a rogues gallery of shady characters, ranging from
Charles Keating to Adnan Khashoggi. The evidence on subsequent stock returns suggests that in
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public battles between short sellers and firms, short sellers usually are vindicated by subsequent
events. The evidence suggests that short sellers play an important role in detecting not just
overpricing, but also fraud. Policy makers might want to consider making the institutional and
legal environment less hostile to short sellers.
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ENDNOTES
1
One complication is that these letters were not always reported in the media in real time (in the
case of Solv-Ex, the February letter was not reported until August). Thus sometimes these letters
are semi-public information, known to large shareholders but not necessarily to all investors.
When examining long-term returns, I use only completely public information (in section V, I
look at event studies and consider the semi-public information as well).
2
There are a small number of firms for which the event date is clear and occurs in a month prior
to the observation date (the first time the event is mentioned in the media). For these firms, they
get included in the long-term returns after the observation date, but the “first 12 months” is in
reference to event date. So if the event occurs in January and I observe it in March, it gets into
long-term return portfolio in March but stays for only 10 months not 12. The same holds true for
firms which are not in CRSP in the event month but are subsequently added. In cases where
there is no well defined event date, I use the observation date for the event date.
3
To eliminate double counting, a firm only is reflected in a given average once in any given
month. For example, a firm is included in the all event calculation in month t if any event
occurred in months t-12 to t-1.
4
I plugged in the delisting return from CRSP into the monthly return sequence in the month after
the last available regular return. So if a firm delists in February, has the last monthly return in
January, and CRSP records a delisting return in April, I plug that delisting return in for February.
Following Shumway (1997), in the very few cases where no delisting return is available, I plug
in -30%.
5
These 125 portfolios are reformed every month based on the market equity, M/B ratio, and
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prior year return from the previous month. However, following Fama and French (1993), the
M/B ratio is only updated annually in July, based on the value as of the previous December.
6
I am grateful to Ken French for this data. All four factors come from his web page. They are
RMRF, the excess market return, SMB (the return on small stocks minus big stocks) and the
HML (the return of high book-market stocks minus low book-market stocks), and a price
momentum factor, UMD (the return of stocks with high prior year returns minus stocks with low
prior year returns). UMD is created by French and is slightly different from the factor
constructed by Carhart (1997).
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Wharton School working paper
Scheinkman, Jose, and Wei Xiong, 2002, Overconfidence and speculative bubbles, Princeton
University working paper.
Shumway, Tyler, 1997, The delisting bias in CRSP data, Journal of Finance 52, 327-340.
Sorescu, Sorin M., 2000, The effect of options on stock prices: 1973 to 1995, Journal of Finance
55, 487-514.
Go down fighting – Page 36
Table I
Distribution of events
The sample is firms which have at least one monthly return in the 12 months following the event.
Description
Comment
Number Number
Events Firms
Claims
conspiracy
Alleges lies
Conspiracy/bear raid/manipulation/illegal
31
31
125
125
21
21
177
161
63
63
Lawsuit
Requests investigation by authorities (usually SEC or
exchange), or claims one is underway, or media reports
that authorities are investigating shorters
Announcement of lawsuit or of retraction based on litigation
35
35
All legal
All legal
98
95
6
6
29
29
7
7
9
9
51
44
326
270
Lies/rumors/planting stories/inaccurate statements
Considering Consider options or conducting investigation using outside
options
counsel or private investigator
All belligerent
Requests
investigation
Exchange
exchange switch/seeking exchange switch
switch
Urge not lend Urges (or "suggests") that shareholders not lend shares to
shorters
Friendly
CEO sets up system to buy own stock, or firm announces
owners
repurchase explicitly in response to shorters, Friendly
owners withdraw shares from lending market, lending
by firm to shareholders, or employee stock ownership
plan buying shares
Other
Split/distribution/halt
technical
All technical
All events
Go down fighting – Page 37
Table II
Characteristics of event firms (percentiles) in month prior to event
Percentile variables in the month prior to event, compared to the universe of CRSP firms. For
firms which do not have data for the month prior to the event, the characteristic is from the
month preceding the first return observation. Size is market equity. M/B is market-book ratio
(market value of equity divided by Compustat book value of equity). The timing of M/B follows
Fama and French (1993) and is as of the previous December year-end. Volume is monthly
turnover (volume divided by shares outstanding) minus the median turnover of all stocks on the
same exchange.
Number
Firms
Claims conspiracy
---------- Percentiles ---------Size M/B Volume Rt-12,t-1 Rt-1,t
31
68
79
95
71
48
125
72
75
88
68
53
21
78
81
89
61
60
161
72
76
89
67
52
Requests investigation
63
67
82
85
70
58
Lawsuit
35
60
84
86
56
49
All legal
95
64
82
85
66
54
6
67
96
68
42
76
29
55
71
81
47
43
Friendly owners
7
76
66
84
75
75
Other technical
9
46
83
89
71
58
44
57
76
81
54
53
270
67
77
87
64
53
Alleges lies
Considering options
All belligerent
Exchange switch
Urge not lend
All technical
All events
Go down fighting – Page 38
Table III
Market adjusted returns subsequent to events
Average monthly market-adjusted returns in percent. Market-adjusted returns are returns minus
the return on the CRSP value weighted index. The t-statistics, in parenthesis, use standard errors
adjusted for the clustering of dates in calendar time.
One month Three month One year
t to t+1
t to t+3
t to t+12
Three years 2 to 3 Years
t to t+36
t+12 to t+36
Claims conspiracy
-3.50 (0.46) -2.11 (0.62) -3.90 (2.47) -2.47 (2.38) -1.43 (1.13)
Alleges lies
-1.62 (0.67) -2.08 (1.61) -1.71 (1.96) -1.31 (2.03) -1.06 (1.55)
Considering options
-5.10 (0.63) -8.19 (2.48) -5.79 (3.44) -2.76 (2.53) -0.82 (0.62)
All belligerent
-2.80 (1.07) -2.66 (2.10) -2.22 (2.75) -1.58 (2.66) -1.16 (1.90)
Requests investigation -5.10 (1.61) -1.75 (0.83) -2.03 (2.12) -1.37 (1.99) -0.98 (1.14)
Lawsuit
-3.48 (1.15) -3.30 (1.03) -2.65 (1.72) -1.44 (1.48) -0.59 (0.48)
All legal
-4.53 (1.94) -2.20 (1.22) -2.27 (2.54) -1.45 (2.22) -0.92 (1.18)
Exchange switch
Urge not lend
3.54 (0.47) -1.69 (0.38) 0.12 (0.06) -2.84 (2.37) -4.26 (2.95)
-4.55 (1.00) -4.66 (1.82) -3.17 (1.65) -2.66 (2.29) -2.14 (1.54)
Friendly owners
-11.46 (0.87) -10.07 (1.66) -4.96 (1.90) 0.23 (0.07) 3.01 (0.60)
Other technical
-8.17 (1.35) -12.39 (2.93) -8.54 (2.61) -5.60 (2.23) -1.52 (0.40)
All technical
-3.86 (1.08) -5.19 (2.62) -2.66 (1.95) -2.04 (2.13) -1.50 (1.14)
All events
-3.27 (1.94) -2.68 (2.71) -2.34 (3.65) -1.51 (2.85) -0.95 (1.61)
Go down fighting – Page 39
Table IV
Calendar time portfolio returns for one-year horizon returns
Monthly returns in percent for the twelve months following an event, calculated using calendar
time portfolios. Portfolios are equal weighted except for last row, which is value weighted.
Market adjusted returns are returns minus the return on the CRSP value weighted index.
Characteristic adjusted are returns minus the returns on a value weighted portfolio of all CRSP
firms in the same size, market-book, and one year momentum quintile. Four-factor alpha is the
intercept from a regression of returns in excess of t-bills on the three factors of Fama and French
(1993), size, value, and market, plus a fourth price momentum factor similar to Carhart (1997).
“N months” is the number of calendar months available for market adjusted returns (the number
may be lower for the other columns). T-statistics in parentheses.
Description
N months
Market
adjusted
Characteristic
adjusted
Fourfactor α
Claims conspiracy
171 -2.93 (1.94) -2.30 (1.70) -1.89 (1.19)
Alleges lies
229 -2.06 (2.22) -1.09 (1.32) -1.76 (1.79)
Considering options
125 -5.46 (3.37) -4.66 (3.05) -4.90 (2.91)
All belligerent
251 -2.23 (2.63) -1.32 (1.80) -1.92 (2.30)
Requests investigation
220 -2.98 (3.23) -2.27 (2.72) -2.41 (2.44)
Lawsuit
179 -3.07 (2.06) -1.95 (1.39) -2.93 (1.88)
All legal
241 -3.10 (3.60) -2.12 (2.72) -2.86 (3.20)
Exchange switch
Urge not lend
53 0.58 (0.27) 1.32 (0.70) -0.83 (0.33)
132 -4.52 (3.07) -4.55 (3.18) -4.00 (2.48)
Friendly owners
51 -4.69 (1.82) -3.15 (1.25) -6.95 (2.19)
Other technical
58 -8.81 (2.45) -7.26 (1.99) -9.93 (2.42)
All technical
180 -2.79 (2.36) -1.97 (1.87) -2.90 (2.33)
All events
257 -2.88 (4.37) -1.91 (3.45) -2.42 (3.86)
All events,
value weighted
257 -1.33 (1.84) -1.26 (1.75) -1.97 (3.26)
Go down fighting – Page 40
Table V
Four factor regressions for one-year horizon returns
Regression results using calendar time monthly returns in percent for the twelve months
following an event. Portfolios are equal weighted except for last row, which is value weighted.
RMRF is returns on the CRSP value weighted portfolio minus T-bill returns. HML is the value
factor (the return of low M/B stocks high M/B stocks), SMB is the size factor (the return on
small stocks minus big stocks), and UMD is the momentum factor (the return of stocks with high
prior year returns minus stocks with low prior year returns). T-statistics in parentheses.
Description
α
RMRF
HML
SMB
UMD
R2
Claims conspiracy
-1.89 (1.19) 1.20 (3.07) -0.26 (0.46) 1.85 (3.90) -1.03 (2.56) 0.26
Alleges lies
-1.76 (1.79) 0.89 (3.48) -0.56 (1.52) 1.36 (4.32) -0.47 (1.77) 0.25
Considering options
-4.90 (2.91) 1.45 (3.95) 0.20 (0.31) 1.59 (2.91) -0.56 (1.07) 0.20
All belligerent
-1.92 (2.30) 1.24 (6.26) -0.42 (1.35) 1.53 (5.57) -0.58 (2.59) 0.36
Requests investigation -2.41 (2.44) 1.27 (5.45) 0.08 (0.23) 0.86 (2.57) -0.78 (2.61) 0.22
Lawsuit
-2.93 (1.88) 0.73 (1.97) -0.60 (1.10) 1.58 (3.19) -0.37 (0.94) 0.16
All legal
-2.86 (3.20) 0.95 (4.49) -0.07 (0.21) 1.44 (4.93) -0.39 (1.61) 0.24
Exchange switch
-0.83 (0.33) 1.59 (2.34) -0.07 (0.07) 1.95 (2.35) 0.79 (0.81) 0.22
Urge not lend
-4.00 (2.48) 0.86 (2.45) 0.25 (0.38) 1.28 (2.29) -0.45 (1.11) 0.15
Friendly owners
-6.95 (2.19) 2.73 (2.86) 1.51 (1.39) 1.79 (1.83) 0.43 (0.43) 0.19
Other technical
-9.93 (2.42) 2.71 (1.96) -2.08 (1.14) 0.81 (0.48) -0.67 (0.41) 0.17
All technical
-2.90 (2.33) 1.28 (4.40) 0.55 (1.10) 1.70 (4.05) -0.02 (0.05) 0.22
All events
-2.42 (3.86) 1.07 (7.20) -0.35 (1.53) 1.51 (7.27) -0.57 (3.38) 0.45
All events,
value weighted
-1.97 (3.26) 1.47 (10.28) 0.02 (0.08) 0.74 (3.72) -0.48 (2.94) 0.43
Go down fighting – Page 41
Figure 1: Volume (measured by percentile turnover) for urge events
90
Percentile turnover
85
80
75
70
-25
-20
-15
-10
-5
0
Day
5
10
15
20
25
20
25
Figure 2: Cumulative returns for urge events
Market-adjusted
3-factor adjusted
15
10
5
-25
-20
-15
-10
-5
0
Day
5
10
15
Go down fighting – Page 42